(1) Field of the Invention
The present invention relates to a method for determining frequency content of a given set of data.
(2) Description of the Prior Art
Much effort has been exerted by analysts to find better ways to process data to obtain desired information contained therein. There are a number of patents which exemplify some of these efforts. These include Statutory Invention Registration No. H374 to Abo-Zena et al. and U.S. Pat. No. 5,262,785 to Silverstein et al.; U.S. Pat. No. 5,299,148 to Gardner et al.; U.S. Pat. No. 5,343,404 to Girgis; and U.S. Pat. No. 5,440,228 to Schmidt.
The Abo-Zena et al. disclosure is related to the identification and resolution of multiple energy sources from signals obtained from an array of sensors. The method relies on an eigenanalysis approach, in series with a minimum variance determination process. There is also an implied requirement to have enough sampled data to represent one complete cycle, or period. In addition, averages of multiple samples are used to increase the input data set.
The Silverstein et al. patent is directed to the identification of doppler frequency shift among moving targets. It utilizes the transmission of pulses and the reception of those reflected signals. The pulsed signals have known characteristics, which includes frequency. Large data sets, which span a complete period, are implied. The method for processing the information used by Silverstein et al. includes an eigenanalysis approach.
The Schmidt patent is directed towards an instantaneous frequency measurement process. It utilizes radar signals (pulses) that are processed via time delays.
The Gardner et al. patent relates to the extraction of communication signals from a signal and noise environment and the determination of the direction of the extracted signals.
The Girgis patent measures phase differences between harmonic components of two input signals.
The field of software engineering is inherently coupled to software measurement techniques, which includes a strong interest in software metric data. This software metric data becomes quite useful in a variety of ways, particularly in measuring project trends. To date, much of the data is represented as raw measurement data, i.e. not preprocessed, and graphically displayed in a standard time series plot. FIG. 1 represents one such graph and displays the Source Lines of Code (SLOC) software metric. This approach however requires the analyst to view numerous plots on an individual basis in an attempt to ascertain project issues, areas of concern, and general software development and project trends. This can become a time consuming effort, generally prone to interpretation and errors.
In the past, there have been numerous efforts to allow an analyst to obtain particular information about a given data set, such as the frequency content. Theoretically, such computations require an infinite data set. Classical estimation techniques, which are based upon Fast Fourier Transform (FFT) techniques, usually require large data sets as well.
There is still needed a method for obtaining frequency information about data so as to provide insight into the periodic nature of the data and thereby to better ascertain general project trends and directions.